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Poster Session
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Forest vegetation information of multispectral image
from space and it's false color display tradeoff
Zhu Qijiang, Liu Jinying
Beijing Normal University, Beijing, China
Abstract
The vegetation response to environment is very sensitive .We need to concern both the quality and the temporal of imagers when selecting the images. In order to compare the performance of land sat MSS TM images and SPOT HRV images with infrared color photographs we select three images windows for processing in Pingquan county, Hebei province.
The problem of selecting a subset of a multi image for enhancement by false color compositing rationing or differencing is generally difficult Usually an intuitive selecting based on physical characteristics of the scenes and on experiences is made multispectral images often exhibit high correlations between spectral bands therefore the redundancy between the components of such multi images may be significant.
- It's common knowledge that there are similarities between some two image bands of land sat TM. A false-color composite image generated by three different bands which are dependent on each other may give more information of physical landscape one way is to choose three difference image bands in which exhibition of lower correlation exists for enhancement by false color composite the correlation matrix of Dawopu digital image window are shown in table 1.
Table 1: Correlation matrix of TM digital image
(expect band 6)
Dawopu image window Pingquan county
| 1. |
1.000 |
| 2. |
0.876 |
1.000 |
| 3. |
0.833 |
0.964 |
1.000 |
| 4. |
0.108 |
0.216 |
0.043 |
1.000 |
| 5. |
0.730 |
0.842 |
0.773 |
0.531 |
1.000 |
| 7. |
0.845 |
0.922 |
0.913 |
0.223 |
0.918 |
1.00 |
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Based on the independence of image data we can distinguish the original six image bands as three image data subset visible image subset (TM1,TM2,TM3,) near infrared image subset (TM4) and mid infrared image subset (TM5,TM7) thus all of possible optional color display subsets of TM images are: (TM4,TM5,TM3),(TM4,TM5,TM2),(TM4,Tm3 TM1); (TM4,TM2,TM7),(TM4,TM3,TM7) and (TM4,TM5,TM7). Color plate 1 (TM4,TM3,TM2) and color plate 2 (TM,TM5,TM3) show two land sat TM false-color composite images of Weizhangzi image window (Pingquan county ) by linear stretch at the same time in different way. The color plate 1 corresponds to standard false color composite The space geometric features in color pears in color plate 2 are almost the same in accuracy but more spectral information appears in color plate 2 than color plate 1 the plate 2 can show the difference not only in crops category but also in water condition and immaturity of crops.
- Orthogonal Transform Based on statistical Features of image data.
The K-L transform to principal components provides a new set of component images that are in correlated and ranked so that each components ha variance less than the previous component. Thus, the K-L transform can be used to reduce the number of spectral components to fewer principal components that account for all but a negligible part of the variance in the original multi spectral image The principal components images may be enhanced combined in to false color composites.
The K-L transform image F° is obtained p components of a multispectral image X by the transformation.
F° = t(X-Mx)
Where X is a vector whose elements are the components at a given location (j, K) in the original multispectral image, Mx is the mean vector of X the components of vector f are the principal components at the location (j,k), T is the P by p unitary matrix whose rows are the normalized eigenvectors tp of the spectral covariance matrix Cx of X arranged in a descending order according to the magnitude of their corresponding eigenvalues, the covariance matrix cx is computed as :
Cx = E [ (X-Mx) (X-M)T ]
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